Multilingual idiomatic phrase translation

ABSTRACT

Embodiments relate to an intelligent computer platform to decipher and translate an analogical phrase. A phrase is translated, yielding a second language phrase. An idiom database is searched for one or more matching idiom phrases. The idiom phrases are ranked according to similarity to the second language phrase and idiom phrase with a highest similarity ranking is outputted as the final output.

BACKGROUND

The present invention relates to natural language processing. Morespecifically, the invention relates to recognizing and resolving ananalogical pattern in a multilingual scenario.

In the field of artificially intelligent computer systems, naturallanguage systems (such as the IBM Watson™ artificially intelligentcomputer system or and other natural language question answeringsystems) process natural language based on knowledge acquired by thesystem. To process natural language, the system may be trained with dataderived from a data source or corpus of knowledge, but the resultingoutcome can be incorrect or inaccurate for a variety of reasons relatingto the peculiarities of language constructs and human reasoning.

For example, analogies are language constructs which enable people totransfer knowledge from one situation or context (the source) to another(the target) based on a conceptual similarity there between, and providepowerful cognitive mechanisms or tools that can be used to explainsomething that is unknown in terms of a related concept that is known tosomeone. At the core of analogical reasoning lies the concept ofsimilarity, but the process of understanding an analogy requiresreasoning from a relational perspective that can be challenging Addingto the challenge is addressing the understanding and relationship acrosslanguages where word-for-word translation may not capture the essence ofthe original statement. In addition, automated systems and other naturallanguage systems which come across an analogy in a question or answercorpus will also have a difficult time with identifying andunderstanding analogies. As a result, existing solutions for efficientlyidentifying and understanding analogies for training and/or use by anatural language processing system are extremely difficult at apractical level.

SUMMARY

The embodiments include a system, computer program product, and methodfor resolving the meaning of an analogy across languages.

In one aspect, a system is provided for use with an intelligent computerplatform for deciphering an analogical phrase. A processing unit isoperatively coupled to memory and is in communication with an artificialintelligence platform. A tool, in communication with the processingunit, is activated by the artificial intelligence platform and employedto decipher a phrase. More specifically, deciphering the phrase includesa parse of a first language phrase into two or more grammaticalsub-components and a determination of one or more terms for ananalogical pattern of the first language phrase. Thereafter, a categoryand a relational term are identified in the analogical pattern, andbased on the identification a translation is performed for eachrelational term to a second language, different from the first language.Following the translation, two idiom dictionary searches are conductive,including a search of a first idiom dictionary in the first language anda search of a second idiom dictionary in the second language for one ormore second language phrases matching the identified relational term. Atleast one or more second language phrases are returned, with each ofthese second language phrases having a matching relational term. Anevaluation is conducted for each returned second language phrase, suchthat a quantifying value is applied to each returned second languagephrase as related to the first language phrase. A phrase is generated asoutput based on the applied quantifying value, the phrase output beingone of the matching second language phrases.

In another aspect, a computer program device is provided for use with anintelligent computer platform for deciphering an analogical phrase. Thedevice has program code embodied therewith. The program code isexecutable by a processing unit to decipher a phrase. More specifically,deciphering the phrase includes a parse of a first language phrase intotwo or more grammatical sub-components and a determination of one ormore terms for an analogical pattern of the first language phrase.Thereafter, the program code identifies a category and a relational termin the analogical pattern, and based on the identification the programcode performs a translation for each relational term to a secondlanguage, different from the first language. Following the translation,the program code conducts two idiom dictionary searches, including asearch of a first idiom dictionary in the first language and a search ofa second idiom dictionary in the second language for one or more secondlanguage phrases matching the identified relational term. At least oneor more second language phrases are returned, with each of these secondlanguage phrases having a matching relational term. The program codeperforms an evaluation for each returned second language phrase, suchthat a quantifying value is applied to each returned second languagephrase as related to the first language phrase. A phrase is generated asoutput based on the applied quantifying value, the phrase output beingone of the matching second language phrases.

In yet another aspect, a method is provided for use by an intelligentcomputer platform for deciphering an analogical phrase. The methodparses a first language phrase into two or more grammaticalsub-components and determines one or more terms for an analogicalpattern of the first language phrase. A category and a relational termin the analogical pattern are identified, and translation is performedfor each relational term to a second language. A first idiom dictionaryis searched in the first language and a second idiom dictionary issearched in the second language for one or more second language phrasesmatching the identified relational term(s). One or more second languagephrases having a matching relational term are returned. A quantifyingvalue is applied to each returned second language phrase as it relatesto the first language phrase. Output is generated in the form of aphrase based on the applied quantifying value, with the phrase outputbeing one of the matching second language phrases.

These and other features and advantages will become apparent from thefollowing detailed description of the presently preferred embodiment(s),taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings reference herein forms a part of the specification.Features shown in the drawings are meant as illustrative of only someembodiments, and not of all embodiments, unless otherwise explicitlyindicated.

FIG. 1 depicts a system diagram illustrating a content and responsesystem connected in a network environment that uses an analogy detectionengine to identify and analyze analogies.

FIG. 2 depicts a block diagram of a processor and components of aninformation handling system such as those shown in FIG. 1.

FIG. 3 depicts a block diagram illustrating a linguistic analysis formultilingual analogy detection and resolution.

FIG. 4 depicts a block diagram illustrating an example application oflinguistic parsing and analysis to identify an analogical pattern of aphrase in the English language and translation of the phrase to theSpanish language.

FIG. 5 depicts a flowchart illustrating a method, utilized by aninformation handling system for multilingual analogy detection andresolution.

FIG. 6 depicts a flow chart illustrating a method, utilizing aninformation system for supporting multilingual deciphering of analogicalphrases.

FIG. 7 depicts a flowchart illustrating an embodiment of a method formultilingual analogy detection and resolution.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentembodiments, as generally described and illustrated in the Figuresherein, may be arranged and designed in a wide variety of differentconfigurations. Thus, the following details description of theembodiments of the apparatus, system, method, and computer programproduct of the present embodiments, as presented in the Figures, is notintended to limit the scope of the embodiments, as claimed, but ismerely representative of selected embodiments.

Reference throughout this specification to “a select embodiment,” “oneembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiments. Thus, appearances of thephrases “a select embodiment,” “in one embodiment,” or “in anembodiment” in various places throughout this specification are notnecessarily referring to the same embodiment.

The illustrated embodiments will be best understood by reference to thedrawings, wherein like parts are designated by like numerals throughout.The following description is intended only by way of example, and simplyillustrates certain selected embodiments of devices, systems, andprocesses that are consistent with the embodiments as claimed herein.

Referring to FIG. 1, a schematic diagram of a natural language processsystem (100) is depicted. As shown, a server (110) is provided incommunication with a plurality of computing devices (180), (182), (184),(186), and (188) across a network connection (105). The server (110) isconfigured with a processing unit in communication with memory across abus. The server (110) is shown with a knowledge engine (150) for naturallanguage processing over the network (105) from one or more computingdevices (180), (182), (184), (186) and (188). More specifically, thecomputing devices (180), (182), (184), (186), and (188) communicate witheach other and with other devices or components via one or more wiredand/or wireless data communication links, where each communication linkmay comprise one or more of wires, routers, switches, transmitters,receivers, or the like. In this networked arrangement, the server (110)and the network connection (105) may enable analogical patternrecognition and resolution for one or more content users. Otherembodiments of the server (110) may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

The knowledge engine (150) may be configured to receive input fromvarious sources. For example, knowledge engine (150) may receive inputfrom the network (105), one or more knowledge bases of corpus (160) ofelectronic documents (162), semantic data (164), or other data, contentusers, and other possible sources of input. In selected embodiments, theknowledge base (160), also referred to herein as corpus, may includestructured, semi-structured, and/or unstructured content in a pluralityof documents that are contained in one or more knowledge data sources orcorpi. The various computing devices (180), (182), (184), (186), and(188) in communication with the network (105) may include access pointsfor content creators and content users. Some of the computing devicesmay include devices for a data source storing the corpus of data as thebody of information used by the knowledge engine (150) to generate ananalogical pattern outcome (104). The network (105) may include localnetwork connections and remote connections in various embodiments, suchthat the knowledge engine (150) may operate in environments of any size,including local and global, e.g. the Internet. Additionally, theknowledge engine (150) serves as a front-end system that can makeavailable a variety of knowledge extracted from or represented indocuments, network accessible sources and/or structured data sources. Inthis manner, some processes populate the knowledge engine (150) with theknowledge engine (150) also including input interfaces to receiverequests and respond accordingly.

As shown, content may be in the form of one or more electronic documentsor files (162) for use as part of the corpus (160) of data with theknowledge engine (150). The corpus (160) may include any structured andunstructured documents, including but not limited to any file, text,article, or source of data (e.g. scholarly articles, dictionary,definitions, encyclopedia references, and the like) for use by theknowledge engine (150). Content users may access the knowledge engine(150) via a network connection or an internet connection to the network(105), and may submit natural language input to the knowledge engine(150) that may effectively translate an analogy present in thesubmissions by content in the corpus of data. As further describedbelow, when a process evaluates a given section of a document forsemantic content, the process can use a variety of conventions to queryit from the knowledge engine (150). Semantic content is content based onthe relation between signifiers, such as words, phrases, signs, andsymbols, and what they stand for, their denotations, or connotation. Inother words, semantic content is content that interprets an expression,such as by using Natural Language (NL) processing. In one embodiment,the process sends well formed content (102), e.g. natural language text,to the knowledge engine (150), so that the content (102) may beinterpreted and the knowledge engine (150) may provide a response in theform of one or more outcomes (104). In one embodiment, the knowledgeengine (150) may provide a response in the form of a ranked list ofoutcomes (104).

In some illustrative embodiments, server (110) may be the IBM Watson™system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive input content (102) which it then parses to extractthe major features of the content (102) that in turn are then applied tothe corpus of data stored in the knowledge base (160). Based onapplication of the content (102) to the corpus of data, a set ofcandidate outcomes are generated by looking across the corpus of datafor portions of the corpus of data that have some potential forcontaining a matching analogical pattern to the submitted content (102).The corpus of data may include data sources with phrase meanings, wordattributes and/or definitions, and language translations of theaforementioned.

In particular, received content (102) may be processed by the IBMWatson™ server (110) which performs analysis on the language of theinput content (102) and the language used in each of the portions of thecorpus of data found during application of the content using a varietyof reasoning algorithms. There may be hundreds or even thousands ofreasoning algorithms applied, each of which performs different analysis,e.g., comparisons, and generates a score. For example, some reasoningalgorithms may look at the matching of terms and synonyms within thelanguage of the input content (102) and the found portions of the corpusof data. Other reasoning algorithms may look at temporal or spatialfeatures in the language, while others may evaluate the source of theportion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response (104) is inferred by the inputcontent (102) based on the specific area of focus of that reasoningalgorithm. Each resulting score is weighted against a statistical model.The statistical model captures how well the reasoning algorithmperformed at establishing the inference between two similar passages fora particular domain during the training period of the IBM Watson™system. The statistical model may be used to summarize a level ofconfidence that the IBM Watson™ system has regarding the evidence thatthe potential response (104), i.e., candidate analogy, is inferred bythe submitted content (102) question. This process may be repeated foreach of the candidate outcomes (104) until the IBM Watson™ system (110)identifies candidate outcomes that surface as being significantlystronger than others and thus, generates a final analogy outcome (104),or ranked set of outcomes, for the input content (102).

To process natural language, the system (110) may include an informationhandling system (152) which uses an analogy detection engine (154) toidentify and analyze analogies by detecting and categorizing analogicalpatterns, generating potential meanings for each detected analogicalpattern from characteristic metadata for terms in the analogicalpattern, and identifying a best meaning for the detected analogicalpattern by analyzing and scoring the potential meanings based on thecharacteristic metadata and terms in the detected analogical pattern.Though shown as being embodied in or integrated with the server (110),the information handling system (152) and/or analogy detection engine(154) may be implemented in a separate computing system (e.g., 190) thatis connected across network (105) to the server (110). Whereverembodied, the analogy detection engine (154) detects and categorizesanalogical patterns, generates potential meanings for each detectedanalogical pattern, and identifies a best meaning for the detectedanalogical pattern by analyzing and scoring the potential meanings basedon the characteristic metadata and terms in the detected analogicalpattern.

In selected example embodiments, the analogy detection engine (154) mayinclude an analogical pattern extractor (170) that is configured toapply NL processing to detect an analogy in a source text segment bymapping parsed terms and phrases from the sentence into one or morepotential analogical patterns. As described in more detail withreference to FIGS. 3 and 4, the analogical pattern extractor (170) mayperform a sentence structure analysis to parse sentences and denoteterms identifying one or more analogical patterns having a sourceanalogic (e.g., source term type and analogical phrase) connected by acomparator to a target analogic (e.g., target analogical phrase andcharacteristic term). For example, the information handling system mayuse a Slot Grammar Logic (SGL) parser to perform parse of a sourcesentence to detect one or more specified analogical patterns (e.g.,“[noun] [verb] like [x] as a [y]” or variations thereof, such as “[noun][verb phrase] [comparator] [adjective] [noun phrase]”). The analogicalpattern extractor (170) may also be configured to apply one or morelearning methods to match a detected analogical pattern to knownpatterns to decide and categorize the source sentence as an analogy.

As shown, the analogy detection engine (154) employs three sub-enginesto support resolution of the analogical pattern, including an anaphoraengine (172), a comparator engine (174), and a correlation engine (176).The anaphora engine (172) functions to resolve the anaphora, and in oneembodiment resolve the relationship of the anaphora to the noun, asidentified by the analogy detection engine (170). The comparator engine(174) functions to resolve the comparator, and more specifically toidentify how the comparator is being employed in the context of theparsed sentence. The correlation engine (176) functions to identify therelationship or connection of keywords in the parsed sentence, and toapply analytic analysis to refine specific terms in the detectedanalogical pattern for entity resolution, ontology, and other termcharacteristics or metadata (e.g., by using the definition of the termsand ontology) for use in determining the outcome or meaning of theanalogy.

The analysis performed by the correlation engine (176) may use the rightor left analogic to search the corpus or knowledge database (160) formatching references to provide evidence for possible meaning to theanalogy. The retrieved evidence references may then be processed tonormalize the reference type or attribute (e.g., for the noun or objector verb term). To support the normalization process, the correlationengine (176) may also use the words in the definition or meaning of theterm in addition to major characteristics associated with the term toassist the pattern correlator in assignment and resolving a term. As aresult of processing the retrieved evidence references, potentialmeanings for each detected analogical pattern are generated from theanalogical pattern terms and associated characteristic metadata.

To evaluate which of the potential meanings best corresponds to thedetected analogical pattern, the analogy detection engine (154) may beconfigured to use the definitions of the terms and ontology in theanalogical pattern to determine and score potential meanings of theanalogy based on the options from the term characteristic alignments andtheir agreements when combined. For example, the analogy detectionengine (154) may include an outcome analyzer (156) for applying anoutcome analysis to the analogical pattern and associated metadata tolook at the noun-verb-object relationships and the categories todetermine the most likely options by scoring the terms and likelihoodthey belong together or are should be associated. The outcome analyzer(156) may apply a learning method for previously similar analogies ornoun-verb relationship in a similar pattern, along with definitionextraction for the verb in relation to the noun/object characteristicsand the comparator/idiomatic used. The meaning of the detectedanalogical pattern may be deduced at the meaning resolver (158) as acombination of the source analogic characteristic and metadata with thetarget analogic outcome, and then presented with the evidence from thecharacteristics and meaning and any corpus references that are used tohelp the determination.

The anaphora engine (172) and the comparator engine (174) generate anidiomatic structure and associated feature sets, which is shown anddescribed in FIG. 1. An analyzer (178) functions as an interface betweenthe generated idiomatic structure(s) and the corpus (160). Morespecifically, the analyzer (178) searches the corpus (160) for evidenceof the pattern, both as an entire analogical pattern, and as a subset ofa pattern. The corpus (160) may be divided into two or more datasources. Multiple data sources allow for the organization of differentcategories of data. For example, one data source may contain completephrases in a first language, while another data source may containphrases in a second language. Likewise, one data source may containphrases while another data source contains idioms. The analyzer (178)applies a score to each feature set according to its incidence in thecorpus (160). An outcome (104) for the analyzer (178) is in the form ofan analogical pattern that matches or closely matches the submittedsentence. More specifically, the outcome (104) is based on the scoring,and in one embodiment, associated ranking of a plurality of potentialoutcomes.

Types of information handling systems that can utilize system (110)range from small handheld devices, such as handheld computer/mobiletelephone (180) to large mainframe systems, such as mainframe computer(182). Examples of handheld computer (180) include personal digitalassistants (PDAs), personal entertainment devices, such as MP4 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer (184),laptop, or notebook, computer (186), personal computer system (188), andserver (190). As shown, the various information handling systems can benetworked together using computer network (105). Types of computernetwork (105) that can be used to interconnect the various informationhandling systems include Local Area Networks (LANs), Wireless Local AreaNetworks (WLANs), the Internet, the Public Switched Telephone Network(PSTN), other wireless networks, and any other network topology that canbe used to interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems may use separate nonvolatile data stores (e.g., server (190)utilizes nonvolatile data store (190 a), and mainframe computer (182)utilizes nonvolatile data store (182 a). The nonvolatile data store (182a) can be a component that is external to the various informationhandling systems or can be internal to one of the information handlingsystems. An illustrative example of an information handling systemshowing an exemplary processor and various components commonly accessedby the processor is shown and described in FIG. 2.

Referring to FIG. 2, a block diagram (200) is provided illustratinginformation handling system. As shown, one or more processors (210) arecoupled to processor interface bus (212), which connects processors(210) to Northbridge (215), which is also known as the Memory ControllerHub (MCH). Northbridge (215) connects to system memory (220) andprovides a means for processor(s) (210) to access the system memory(220). In the system memory (220), a variety of programs may be storedin one or more memory devices, including an analogy detection engine(221) which may be invoked to detect an analogy by parsing or breaking asentence into a discrete analogical pattern and then use definitions ofthe terms in the analogical pattern(s) to determine the potentialmeanings of the analogy. Graphics controller (225) also connects toNorthbridge (215). In one embodiment, PCI Express bus (218) connectsNorthbridge (215) to graphics controller (225). Graphics controller(225) connects to display device (230), such as a computer monitor.

Northbridge (215) and Southbridge (235) connect to each other using bus(219). In one embodiment, the bus is a Direct Media Interface (DMI) busthat transfers data at high speeds in each direction between Northbridge(215) and Southbridge (235). In another embodiment, a PeripheralComponent Interconnect (PCI) bus connects the Northbridge and theSouthbridge. Southbridge (235), also known as the I/O Controller Hub(ICH) is a chip that generally implements capabilities that operate atslower speeds than the capabilities provided by the Northbridge (215).Southbridge (235) typically provides various busses used to connectvarious components. These busses include, for example, PCI and PCIExpress busses, an ISA bus, a System Management Bus (SMBus or SMB),and/or a Low Pin Count (LPC) bus. The LPC bus often connectslow-bandwidth devices, such as boot ROM (296) and “legacy” I/O devices(298) (using a “super I/O” chip). The “legacy” I/O devices (298) caninclude, for example, serial and parallel ports, keyboard, mouse, and/ora floppy disk controller. Other components often included in Southbridge(235) include a Direct Memory Access (DMA) controller, a ProgrammableInterrupt Controller (PIC), and a source device controller, whichconnects Southbridge (235) to nonvolatile source device (285), such as ahard disk drive, using bus (284).

ExpressCard (255) is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard (255) supports both PCI Expressand USB connectivity as it connects to Southbridge (235) using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge (235)includes USB Controller (240) that provides USB connectivity to devicesthat connect to the USB. These devices include webcam (camera) (250),infrared (IR) receiver (248), keyboard and trackpad (244), and Bluetoothdevice (246), which provides for wireless personal area networks (PANs).USB Controller (240) also provides USB connectivity to othermiscellaneous USB connected devices (242), such as a mouse, removablenonvolatile source device (245), modems, network cards, ISDN connectors,fax, printers, USB hubs, and many other types of USB connected devices.While removable nonvolatile source device (245) is shown as aUSB-connected device, removable nonvolatile source device (245) could beconnected using a different interface, such as a Firewire interface,etc.

Wireless Local Area Network (LAN) device (275) connects to Southbridge(235) via the PCI or PCI Express bus (272). LAN device (275) typicallyimplements one of the IEEE 802.11 standards for over-the-air modulationtechniques to wireless communicate between information handling system(200) and another computer system or device. Extensible FirmwareInterface (EFI) manager (280) connects to Southbridge (235) via SerialPeripheral Interface (SPI) bus (278) and is used to interface between anoperating system and platform firmware. Optical source device (290)connects to Southbridge (235) using Serial ATA (SATA) bus (288). SerialATA adapters and devices communicate over a high-speed serial link. TheSerial ATA bus also connects Southbridge (235) to other forms of sourcedevices, such as hard disk drives. Audio circuitry (260), such as asound card, connects to Southbridge (235) via bus (258). Audio circuitry(260) also provides functionality such as audio line-in and opticaldigital audio in port (262), optical digital output and headphone jack(264), internal speakers (266), and internal microphone (268). Ethernetcontroller (270) connects to Southbridge (235) using a bus, such as thePCI or PCI Express bus. Ethernet controller (270) connects informationhandling system (200) to a computer network, such as a Local AreaNetwork (LAN), the Internet, and other public and private computernetworks.

While FIG. 2 shows one information handling system (200), an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory. In addition, an information handling system need not necessarilyembody the north bridge/south bridge controller architecture, as it willbe appreciated that other architectures may also be employed.

Referring to FIG. 3, a block diagram (300) is provided illustratinglinguistic parsing and analysis to identify an analogical pattern. Asdisclosed herein, the linguistic analysis processing may be performed bya natural language processing system, such as the information handlingsystem (152) as shown in FIG. 1, or any suitable information handlingsystem. A sentence parsing tool, such as but not limited to SGL, isapplied to separate a received sentence into its constituent parts tofind the sentence parts and location sequence. For example, the sentenceis received and a parsing process applied to the sentence identifies thesentence terms and structure. As shown, the received sentence is matchedwith an identified analogical pattern (310). More specifically, thematching patterns includes a term type (312), an analogical phrase(314), also referred to herein as a left or first analogical phrase, acomparator (316), an analogical phrase (318), also referred to herein asa right or second analogical phrase, and a characteristic term (320).The term type (312) and the first analogical phrase (314) are referredto herein as a first or left analogic (302), and the second analogicalphrase (318) and characteristic term (320) are referred to herein as asecond or right analogic (304). Accordingly, through the sentenceparsing tool, the structure of the sentence is identified and matchedwith an analogical pattern.

The sentence parsing tool or related SGL process is applied to thesentence to further separate the sentence components into constituentparts (330)-(338) and associated location sequence. The sentence parsingtool identifies the terms and structures as including a subject nounterm (330), a verb phrase (332), an adjective or idiom phrase (334), apreposition/verb phrase (336) and a noun/object term (338). Each of theidentified terms and structure may include various grammatical terms.For example, the noun term (330) may include any one of an entity,ontology type, subject, hypernym, or hyponym; the verb phrase (332)and/or preposition/verb phrase (336) may include any one of a verbphrase, verb definition words, and correlated keyword; the adjective oridiom phrase (334) may include any one of an idiom, idiomatic phrase,meaning, meaning structure, or resolved anaphora; the preposition/verbphrase (336) may include a comparator or resolved comparator; and thenoun/object (338) may include any one of a noun phrase, adjective,definition words, and correlated keywords.

In an example shown in FIG. 3, the terms “Juan”, “cone”, “como”, “elviento”, and “rapido” may be stored in separate data locations, e.g.data sources, within data source or categorized differently, or in oneembodiment, they may be stored in the same data location. In oneembodiment, data sources may be organized by language so as to enableparallel searching of a phrase. In the example shown in FIG. 3, “Juan”(342) is identified as a possible subject/person (330), “cone” (344) isidentified as a possible verb (332) defined in the English language as“run”, “como” (346) is identified as the idiom (334), and is defined inthe English language as “like”, “el viento” (348) is identified as apossible noun/object (338), which translates in the English language to“the wind” , and “rapido” (350) is identified as a possible adjective(334), and in the English language is translated to “quickly”. The termsmay be identified by manually inputting them into the respective datasources or categories, or by machine-learning program from previouslypopulated data sources with encountered phrases. Depending on thelanguage in the sentence, the grammatical parsing may generate differentsentence terms and/or structures. To further assist with analogydetection processing, each of the parsed sentence terms or parts(330)-(338) may be further analyzed for entity resolution, ontology,and/or associated characteristics in order to generate patterncharacteristic metadata for use in refining the terms of the detectedanalogical pattern.

As shown, the parsed sentence terms or parts (312)-(320) of the sentence(310) are categorized into an analogical pattern in the source language,shown herein as a first or left analogic (302) having the first termtype (312) and the first analogical phrase (314) that is connected by acomparator (316) to a second or right analogic (304) shown herein ashaving the second analogical phrase (318) and the characteristic term(320). The analogical pattern categorization process may be implementedby applying a learning method to match various combinations of theparsed sentence terms or parts (312)-(320) to one or more knownanalogical patterns to decide and categorize the sentence or phrase(310) as an analogy. If the syntactic structure of a combination patternof parsed sentence terms or parts (312)-(320) match a known analogicalpattern in the source language, the relevant analogy terms and patterntypes in the source language are identified as a candidate meaning andassociated for use in subsequent processing. For example, the detectedanalogy pattern may be (noun like, as a, etc), and the object noun termmay be pulled and associated with the object verb phrase. The idiomaticphrase (360) is then searched on a source language idiom dictionary(370), also referred to herein as a first language idiom dictionary. Thelanguage idiom dictionary can be in the form of a corpus or data sourcecontaining idiom phrases in the target language. The phrase (360) mayalso be cross checked with a target language idiom dictionary (380),also referred to herein as a second language idiom dictionary, forequivalent idioms in the target language. The target language shown inFIG. 3 is shown to be English, however, the target language idiomdictionary (380) may be any language to which the translation is beingmade.

Referring to FIG. 4, a block diagram (400) is provided illustratingapplication of linguistic parsing and analysis to identify an analogicalpattern of a phrase in the English language and translation of thephrase to the Spanish language. In the example shown, “John runs like afast wind” (440) is the received source sentence. A sentence parsingtool, such as but not limited to SGL, is applied to separate thereceived sentence into its constituent parts to identify the sentenceparts and location sequence in the source language, e.g. the originallanguage of the received sentence. The received sentence (440) ismatched with an identified analogical pattern (410). Similar to thepattern shown in FIG. 3, the matching patterns includes a term type(412), an analogical phrase (414), also referred to herein as a left orfirst analogical phrase, a comparator (416), an analogical phrase (418),also referred to herein as a right or second analogical phrase, and acharacteristic term (420). The term type (412) and the first analogicalphrase (414) are components of the first or left analogic (402), and thesecond analogical phrase (418) and characteristic term (420) arecomponents of the second or right analogic (404). Accordingly, throughthe sentence parsing tool, the structure of the sentence in the sourcelanguage is identified and matched with an analogical pattern.

With regard to the phrase (440), the word “John” is identified to be anoun or subject (430) and correlated to be a term type (412). The word“John” is also individually translated to a target language, i.e.Spanish, to produce the translation “Juan” (442) as the Spanishequivalent of the name “John”. The same step is repeated for the othercomponents of the phrase: the word “runs” is identified to be a verbphrase (432), correlated to be an analogical phrase (414), andtranslated to the Spanish word “cone” (444); the word “like” isidentified to be an idiom phrase (434), correlated to be a comparator(416), and translated to the Spanish word “como” (446); the word “fast”is identified to be an adjective (434), and translated to the Spanishword “rapido” (450); the word “wind” is identified to be a noun orobject (438), correlated to be a characteristic term (420), andtranslated to the Spanish word “viento” (448). As shown in FIG. 4, somephrases received in the source language may incorporate other elementssuch as a verb phrase (436). For example, replacing the original phrasefor the phrase “John runs like a flowing wind” replaces the adjective(434), i.e. “fast”, for the verb (436) “flowing”. In this example, theword “flowing” would be identified to be a verb phrase (436) andcorrelated to be an analogical phrase (418).

The sentence components are determined to have definitions orattributes. The definitions or attributes are translated from the sourcelanguage to the target language. In the example shown herein, theidiomatic phrase (460) is presented to an idiom dictionary in the sourcelanguage (470), also referred to herein as a first language idiomdictionary or a source language idiom dictionary, to search for ameaning of the idiom. The source language idiom dictionary can be in theform of a corpus or data source containing idiom phrases in the targetlanguage. The meaning obtained from the source language dictionary (470)is translated to the target language and converged with the translateddefinitions and attributes from the components of the parsed phrase(440). The result of the convergence is a target phrase in the targetlanguage, that incorporates the translated meaning, definitions, andattributes of the original phrase or sentence (440). The target languageshown in FIG. 4 is shown to be English and the translation is shown tobe Spanish, however, the target language idiom dictionary (480)), alsoreferred to herein as a second language idiom dictionary, may be anylanguage to which the translation of the source sentence is being made.

Embodiments associated with the sentence parsing and analogy translationmay be in the form of a system with an intelligent computer platform fordeciphering analogical phrases. A processing unit is operatively coupledto memory and is in communication with an artificial intelligenceplatform. A tool or analogy detection engine (154), also incommunication with the processing unit, is employed to decipher a phraseupon activation by the artificial intelligence platform. The procedureof deciphering the phrase includes parsing a phrase received in a firstlanguage into two or more grammatical sub-components, identifying acategory for each parsed grammatical sub-component, and utilizing theidentified categories to determine two or more attributes anddefinitions of the parsed grammatical components in a first language,also referred to herein as a source language. The tool translates thetwo or more attributes and definitions to a second language, alsoreferred to herein as a target language, thus yielding second languageattributes and definitions.

A search is conducted in a first data source for a meaning of the firstlanguage phrase, whereby the first data source stores phrases andcorresponding meanings in the first language. Thereafter, the meaning istranslated to the second language thus yielding a second languagemeaning. The tool (154) converges the second language meaning with thesecond language attributes and definitions to produce a target phrase inthe second language. A search of the target phrase is conducted in asecond data source to find similar phrases in the second language. Basedon the results of the search, the tool applies a ranking to the secondlanguage phrases according to similarity to the target phrase, and afinal phrase is outputted from the ranking. More specifically, the finalphrase identifies the second language phrase with the highest similarityin the ranking to the target language phrase. In one embodiment, theinformation handling system (152) applies the final phrase to firstlanguage phrase, and more specifically annotates the first languagephrase, in an artificial intelligence system.

Alternatively, the tool (154) may commence deciphering the phrase in thesource language by translating a first language phrase, e.g. sourcelanguage, to a second language phrase, e.g. target language, whereby thetranslation is performed for each individual word within the firstlanguage phrase. The tool (154) further searches a data source for atleast one phrase pattern in the data source that matches the secondlanguage phrase. If the tool does not find any matches in the datasource, the second language phrase becomes the final output. However, ifthe tool does find a match in the data source, an idiom data source,also referred to herein as a corpus, is searched for one or more idiomphrases that match the second language phrase. If the tool finds nomatches in the idiom data source, the second language phrase isoutputted as the final output. If the tool finds at least one match inthe idiom data source, a ranking is applied to the idiom phrases thatmatch according to similarity of each found phrase to the secondlanguage phrase. The matching idiom phrase with a highest similarity inthe ranking is outputted as the final output. The details of the processundertaken by the tool in this embodiment are shown and described belowwith reference to FIG. 6.

Embodiments may also be in the form of a computer program device for usewith an intelligent computer platform in order to assist the intelligentcomputer platform to decipher analogical phrases. The device has programcode embodied therewith. The program code is executable by a processingunit to parse a phrase received in a first language, also referred toherein as a source or native language, into two or more grammaticalsub-components, identify a category for each parsed grammaticalsub-component, and utilize the identified categories to determine two ormore attributes and definitions of the parsed grammatical components inthe first language. The processing unit translates the two or moreattributes and definitions to a second language, also referred to hereinas a target language, thus yielding second language attributes anddefinitions. A search is conducted in a first data source for a meaningof the first language phrase, whereby the first data source storesphrases and corresponding meanings in the first language. The meaning isthen translated to the second language thus yielding a second languagemeaning. The processing unit then converges the second language meaningwith the second language attributes and definitions to produce a targetphrase in the second language. A search of the target phrase isconducted in a second data source to find similar phrases in the secondlanguage. Based on the results of the search, the processing unitapplies a ranking to the second language phrases according to similarityto the target phrase, and a final phrase is outputted from the ranking.More specifically, the final phrase identifies the second languagephrase with the highest similarity in the ranking to the target languagephrase.

In one embodiment, program code, executable by a processing unit, isemployed to translate a first language phrase to a second languagephrase, whereby the translation is performed for each individual wordwithin the first language phrase. The program code searches a datasource, e.g. corpus, for at least one phrase pattern that matches thesecond language phrase. If the search does not yield any matches in thedata source, the second language phrase is outputted as a final output.However, if the search yields a match in the data source, an idiom datasource is searched for one or more idiom phrases that match the secondlanguage phrase. If the search does not produce any matches in the idiomdata source, the second language phrase is outputted as the finaloutput. If the search produces at least two matches in the idiom datasource, a ranking is applied to the idiom phrases that match with theranking based on similarity between each matching phrase to the secondlanguage phrase. The matching idiom phrase with a highest similarity inthe ranking is outputted as the final output.

With respect to FIG. 5, a flow chart (500) is provided illustrating anaspect of embodiments that may also take the form of a method for use byan intelligent computer platform for deciphering analogical phrases. Asshown, a phrase in a first language is parsed into two or moregrammatical sub-components in the first language (502), and a categoryfor each parsed grammatical sub-component is identified (504). Utilizingthe identified categories, two or more attributes and definitions of theparsed grammatical components in the first language are determined (506)and the attributes and definitions are translated to a second language,thus yielding second language attributes and definitions (508). A searchis conducted in a first data source for a meaning of the first languagephrase (510), whereby the first data source stores phrases andcorresponding meanings in the first language. The meaning is thentranslated to the second language thus yielding a second languagemeaning (512). The second language meaning is converged with the secondlanguage attributes and definitions to produce a target phrase in thesecond language (514). A search of the target phrase is conducted in asecond data source to find similar phrases in the second language (516).Based on the results of the search, a ranking is applied to the secondlanguage phrases according to similarity to the target phrase (518), anda final phrase is outputted from the ranking (520). More specifically,the final phrase identifies the second language phrase with the highestsimilarity in the ranking to the target language phrase.

Referring to FIG. 6, a flow chart (600) is provided illustrating anaspect of embodiment that may also take the form of a method for use byan intelligent computer platform for deciphering analogical phrases. Asshown, a phrase in a first language, also referred to herein as a sourcelanguage, is parsed into two or more grammatical sub-components in thefirst language (602). The parsing at step (602) detects the analogicalphrase and analogical outcome in the source language, therebydetermining the terms for an analogical pattern of the source languagephrase. In one embodiment, the parsing at step (602) is modified tohandle grammar rules, including adjective and noun placements.Similarly, in one embodiment, the parsing at step (602) is modified tohandle verb conjugations, including changing the verb conjugation to anequivalent term, denote the tense, maintain tense parsingcharacteristics, etc. Accordingly, the parsing of the source languagephrase identifies grammatical aspects of the phrases, including but notlimited to, one or more grammatical sub-components.

Following step (602), both a category and one or more relational termsin the analogical pattern of the source language phrase are identified(604). More specifically, attributes and meaning of the analogicalterm(s) and verb(s) in the source language are expanded, and grammaticalterms and idiomatic phrases are produced. The identification supportsassignment of characteristics to meaning of the terms and phrases in thesource language. In one embodiment, the identification at step (604)includes access to a data store in the source language to determine ameaning of the idiomatic phrases, e.g. searching for an idiom thatmatches the relational term. Using the ascertaining meanings from step(604), a relationship to the outcome is attempted for each expandedattribute and idiom (606). More specifically, for the analogy underconsideration, the part of the source language that makes the analogy isdetermined. The following is an example of a source language analogyusing the term pear.

EXAMPLE

The word pear is understood in English to be a fruit, and characterizingterms may include sweet, ripe, and tasty. A weak analogy is the analogysweet as a constant attribute of taste since a sweet taste is sometimesgood but healthy is always good. A strong analogy is ripe as a changingattribute of the fruit. Ripe is the best state of fruit and health is abest state of a person. A moderate analogy is tasty as a constantattribute.

Based on the meanings and attempted relationship(s), the attributes andrelational terms are translated to a target language (608). Based on thetranslation, an idiom bank in the target language is employed to searchfor the analogy in the target language with the same or a similaroutcome as the analogy in the source language (610). More specifically,the idiom bank consultation at step (610) is looking for an equivalentrelational term in the target language to identify a potential match ofthe analogy. Accordingly, the source language idiom bank is searched,see step (604), and the target language idiom is searched, see step(610), to contribute to the production of a matching analogy phrasemeaning.

Based on the target language idiom bank search at step (610), one ormore target language phrases are identified. The target language phrasesare subject to evaluation in order to determine a match to the sourcelanguage analogy. More specifically, for each identified target languagephrase, the phrase is parses into two or more grammaticalsub-components, and a category for each parsed target language phrasesis identified (612). The parsing at step (612) produces attributes foreach target phrase. For each of these attributes, means and verbs of theanalogical terms are expanded in the target language (614). For example,the term fit is expanded to include adapted, proper, ready, wrong, andconvenient, the term right is expanded to include correct, not wring,and convenient, and the term rain is expanded to include falling water,and weather. These examples are for descriptive purposes and should notbe considered limiting. It is then determined how well a potential matchfits to the source language analogy (616). More specifically, for eachexpanded attribute in the target language a relationship is attempted tothe outcome. In one embodiment, the attempted matching at step (616)includes determining what part of the target language makes the analogy.For example, ‘fit fiddle’ may be extrapolated to mean a musicalinstrument can be tuned to an optimal state since fit means proper orready. As such, this may be considered a strong match. In anotherexample, ‘right rain’ may be extrapolated to a state of rain. However,it is understood that rain does not have a correct state, and as such,this may be considered a weak match. Accordingly, the attempted matchgenerates one or more potential analogy translations, if any.

Following step (616), one or more matches and associated linguisticplacement of the set of terms in the matches are evaluated (618), and inone embodiment, a score is attached. For example, returning to step(606), one or three levels of scores are shown applied to the analogies,although the quantity of levels should not be considered limiting. Anumerical value may be assigned to each level, and a statisticalevaluation may be employed based on the numerical values. For example,in one embodiment, the number 3 may be assigned to a strong analogy, thenumber 2 may be assigned to a moderate analogy, and the number 1 may beassigned to a weak analogy, and the statistical evaluation may be in theform of an average of the score assignment in view of the quantity ofanalogies being evaluated. Other forms of statistical evaluation may becontemplated, and the example shown herein should not be consideredlimiting. The matching process or algorithm is employed to generate amatch, or in one embodiment, an accurate or near accurate translation ofthe analogy (620). In one embodiment, the match is the form of theanalogy translation with the highest score from the statisticalevaluation. In one embodiment, and as shown herein a matching targetlanguage phrase is entered into the source language idiom bank as amatch, such that a subsequent use of the phrase may produce a possiblematch from a prior iteration (622), e.g. creating a cache of thematching phrase in the idiom bank. Accordingly, following the productionof a match of the idiom meaning in the idiom dictionaries, a score andranking of the linguistic terms and meanings in the target language isproduced.

Referring to FIG. 7, a flow chart (700) is provided illustrating amethod utilized by an information handling system comprising a processorand memory, to decipher an analogical phrase. As shown, a first languagephrase is translated to a second language phrase (702). The translationis performed for each individual word within the first language phrase.A data source is searched for at least one phrase pattern that matchesthe translated second language phrase (704). Based on the search, it isdetermined whether any matches exist between the second language phraseand a phrase pattern within the data source (706). If no matches arefound in the data source, the second language phrase, as a literaltranslation, is outputted as a final output (710). In one embodiment,the second language phrase may also be added to an idiom data source inorder to promote machine learning. Accordingly, as phrase translationsare created, they are added to the data source so that futuretranslations may access these translations.

If at step (706) a match is found in the data source, an idiom datasource is searched for one or more idiom phrases that match the secondlanguage phrase (708). It is then determined if there are any matchesbetween second language phrase and the idiom phrases found in the idiomdata source (712), e.g. idiom corpus or idiom dictionary. If no matchesare found in the idiom data source, the second language phrase isoutputted as the final output (710). At this stage, the second languagephrase may also be added to the idiom data source in order to promotemachine learning for future translation needs. If at least one match isfound in the idiom data source, as evidenced by a positive response tothe determination at step (712), the idiom phrases are ranked accordingto similarity to the second language phrase, and idiom phrase with ahighest similarity ranking is outputted as the final output (714).

It will be appreciated that there is disclosed herein a system, method,apparatus, and computer program product for evaluating natural languageinput, detecting an analogical pattern at an information handlingsystem, and accurately translating the detected pattern . As disclosed,the system, method, apparatus, and computer program product applynatural language processing to an information source to identify ananalogical pattern in the input, with the identification including asubject term, a first verb phrase, a comparator term, a second verbphrase, and an object term. For example, the first analogical patternmay include a first analogic (which includes a subject noun term and afirst verb), an adjective/idiom comparator term, and a second analogic(which includes a second verb and a noun object term).

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles.

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.Thus embodied, the disclosed system, a method, and/or a computer programproduct is operative to improve the functionality and operation of amachine learning model based on pattern dissection of analogies andtheir meanings to determine outcomes, including an extendedcharacteristic of key items in the analogical patterns.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It will be appreciated that, although specific embodiments of theinvention have been described herein for purposes of illustration,various modifications may be made without departing from the spirit andscope of the invention. In particular, the natural language processingmay be carried out by different computing platforms or across multipledevices. Furthermore, the data source, data store and/or corpus may belocalized, remote, or spread across multiple systems. Accordingly, thescope of protection of this invention is limited only by the followingclaims and their equivalents.

What is claimed is:
 1. A computer system comprising: a processing unitoperatively coupled to memory; an artificial intelligence platform, incommunication with the processing unit; a tool in communication with theprocessing unit to decipher a phrase upon activation by the artificialintelligence platform, including the tool to: parse a first languagephrase into two or more grammatical sub-components using a linguisticparser; determine one or more terms for an analogical pattern of thefirst language phrase; identify a category and a relational term in theanalogical pattern; perform a translation for each relational term to asecond language; search a first idiom dictionary in the first languageand a second idiom dictionary in the second language for one or moresecond language phrases matching the identified relational term; returnone or more second language phrases having a matching relational term;and apply a quantifying value to each returned second language phrase asrelated to the first language phrase; and a phrase output based on theapplied quantifying value, the phrase output being one of the matchingsecond language phrases.
 2. The system of claim 1, wherein the parse ofthe first language phrase further comprises the tool to incorporate oneor more grammar rules, including an adjustment of adjective and nounplacement in the first language phrase.
 3. The system of claim 2,further comprising the tool to change a verb conjugation in the parsedfirst language phrase to an equivalent term and to denote tense as aparse characteristic.
 4. The system of claim 1, wherein the relationalterm is selected from the group consisting of: noun, subject, verb,adjective, object, characteristic term and comparator.
 5. The system ofclaim 1, further comprising the tool to parse the returned one or moresecond language phrases into two or more grammatical components in thesecond language and identify the category for each parsed component. 6.The system of claim 5, further comprising the tool to expand anattribute and meaning of the parsed second language components in thesecond language.
 7. The system of claim 6, wherein application of thequantifying value to each matching second language phrase, furthercomprises the tool to apply the quantifying value to each expandedattribute, wherein the quantifying value includes an accountingassociated with placement of the components within the first and secondlanguage phrases.
 8. A computer program product to decipher a phrase,the computer program product comprising a computer readable storagedevice having program code embodied therewith, the program codeexecutable by a processing unit to: parse a first language phrase intotwo or more grammatical sub-components using a linguistic parser;determine one or more terms for an analogical pattern of the firstlanguage phrase; identify a category and a relational term in theanalogical pattern; perform a translation for each relational term to asecond language; search a first idiom dictionary in the first languageand a second idiom dictionary in the second language for one or moresecond language phrases matching the identified relational term; returnone or more second language phrases having a matching relational term;and apply a quantifying value to each returned second language phrase asrelated to the first language phrase; and a phrase output based on theapplied quantifying value, the phrase output being one of the matchingsecond language phrases.
 9. The computer program product of claim 8,wherein the parse of the first language phrase further comprises programcode to incorporate one or more grammar rules, including an adjustmentof adjective and noun placement in the first language phrase.
 10. Thecomputer program product of claim 9, further comprising program code tochange a verb conjugation in the parsed first language phrase to anequivalent term and to denote tense as a parse characteristic.
 11. Thecomputer program product of claim 8, wherein the relational term isselected from the group consisting of: noun, subject, verb, adjective,object, characteristic term and comparator.
 12. The computer programproduct of claim 8, further comprising program code to parse thereturned one or more second language phrases into two or moregrammatical components in the second language and identify the categoryfor each parsed component.
 13. The computer program product of claim 12,further comprising program code to expand an attribute and meaning ofthe parsed second language components in the second language.
 14. Thecomputer program product of claim 13, wherein application of thequantifying value to each matching second language phrase, furthercomprises program code to apply the quantifying value to each expandedattribute, wherein the quantifying value includes an accountingassociated with placement of the components within the first and secondlanguage phrases.
 15. A method to computationally resolve a meaning ofan analogy, the method comprising: parsing a first language phrase intotwo or more grammatical sub-components using a linguistic parser;determining one or more terms for an analogical pattern of the firstlanguage phrase; identifying a category and a relational term in theanalogical pattern; performing a translation for each relational term toa second language; searching a first idiom dictionary in the firstlanguage and a second idiom dictionary in the second language for one ormore second language phrases matching the identified relational term;returning one or more second language phrases having a matchingrelational term; and applying a quantifying value to each returnedsecond language phrase as related to the first language phrase; and aphrase output based on the applied quantifying value, the phrase outputbeing one of the matching second language phrases.
 16. The method ofclaim 15, further comprising incorporating one or more grammar rulesinto the parsing of the first language phrase, including adjustingadjective and noun placement in the first language phrase.
 17. Themethod of claim 16, further comprising changing a verb conjugation inthe parsed first language phrase to an equivalent term and denotingtense as a parse characteristic.
 18. The method of claim 15, furthercomprising parsing the returned one or more second language phrases intotwo or more grammatical components in the second language andidentifying the category for each parsed component.
 19. The method ofclaim 18, further comprising expanding an attribute and meaning of theparsed second language components in the second language.
 20. The methodof claim 19, wherein application of the quantifying value to eachmatching second language phrase, further comprises applying thequantifying value to each expanded attribute, wherein the quantifyingvalue includes an accounting associated with placement of the componentswithin the first and second language phrases.